Proceedings of 2014 IEEE International Conference on Mechatronics and Automation August 3 - 6, Tianjin, China
Vibration Monitoring System of Ships Using Wireless Sensor Networks Xin-yan Xiong, Fei Wei, Jing-wei Li, Mei Han, Dong-hai Guan Department of Automation Harbin Engineering University Harbin, Heilongjiang Province, China
[email protected] Wireless sensors are gaining popularity for the monitoring of large structures because they are inexpensive and easy to install [5]. Recent years, there are a number of successful field validation studies of wireless monitoring systems installed on different kinds of structures. Wireless sensors are capable of collecting data. Without wires, wireless sensors often depend on internally stored power for operation. Inefficient use of wireless sensors will deplete this finite energy source rapidly. As a result, we have to replace batteries much more frequently; repeated battery replacement would quickly waste any cost savings realized by using wireless sensors. In particular, the wireless communication module serves as the link between a wireless sensor and the outside world, thus it consumes most of energy on the sensor [7] [8]. Therefore, it is important to be selective about how the wireless communication channel is used. This fact has led to research into parallel and distributed algorithms in which data processing is actually accomplished within the wireless sensor, allowing them to broadcast a relatively small amount of processed data as opposed to a massive amount of highbandwidth raw data. Distributed processing is possible in wireless sensing because of the computational processing power that is collocated with the sensor. Another motivation for distributed processing is bandwidth limitations over the wireless communication channel. Wireless communications are inherently limited by over-the-air transmission rates and the amount of wireless bandwidth available in the crowded unlicensed industrial, scientific, and medical (ISM) bands [4].
Abstract - For the managers to improve their knowledge regarding the vibration condition of the ship they manage, a dense array of wireless sensors installed on the ship could provide ample amounts of data for monitoring vibration condition. The wireless sensor networks offer a distributed computing paradigm that allows sensor to self interrogate acceleration data. In particular, the proposed wireless sensor is constructed using a compact two layer printed circuit board. Furthermore, the wireless sensor utilizes an implementation of the IEEE 802.15.4 standard for wireless networks allowing it to participate in scalable, star network that is adaptable to network changes without requiring reprogramming. Farthermore, a data collection and analysis software with a database is built to realized more data-processing. Index Terms - wireless sensor networks; vibration monitoring; FFT
I. INTRODUCTION Nowadays, with the development of ship building technology, some new-built ships are made in aluminum for decreasing the weight so that ships could get highperformance. However, high-performance aluminum ships also provide a number of operational challenges to the ship engineering community. One of the problems is the influence of vibration. As we all know, with the engine working in vary speed, the engine would cause vibration start from engine room and spread across the hull. When the vibration is greater than the design of vibration-tolerance, it would damage the hull structure health, especially for the ships build in aluminum. A vibration monitoring system is needed. A Wireless Sensor Networks (WSN) consists of spatially distributed sensor to monitor physical or environmental condition. A sensor network is composed of a large number of sensor nodes [1]. The increasing interest in wireless sensor networks can be promptly understood simply by thinking about what they essentially are: a large number of small sensing self-powered nodes which gather information or detect special events and communicate in a wireless network, with the end goal of handing their processed data to a data center like PC [3].
III. WIRELESS SENSOR HARWARE DESIGN Each node of the wireless sensor network has typically several parts: a radio transceiver with an internal antenna or connection to an external antenna, a micro-controller, an electronic circuit for interfacing with the sensors and an energy source, usually a battery. A sensor node might be small in size so it would be easy to place. The cost of sensor nodes should be inexpensive so they can be placed in a large number to collect data. Size and cost constraints on sensor nodes result in corresponding constraints on resources such as energy, memory, computational speed and communications bandwidth. The topology of the WSNs can vary from a simple star network to an advanced multi-hop wireless mesh network. The propagation technique between the hops of the network can be routing or flooding.
II. WIRELESS SENOSR NETWORKS The Wireless Sensor Networks is built of "nodes". The number of nodes may range from a few to several hundreds or even thousands, where each node could connect to the other sensors, and send data to the base server.
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The wireless sensor utilized in this study is the BEE made by Harbin Engineering University. The BEE consists of three modules: the computational core module, the accelerometer module, and the communication module.
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Fig.2 the computational module
B. Sensor module The sensor module is responsible for collecting accelerations in 3-Axis. It consists of an InvenSence MPU6050 accelerometer. The accelerometer combines a 3axis gyroscope, 3-axis accelerometer, and a Digital Motion Processor(DMP) all in a small 4h4h0.9mm package. But in this BEE, we use the accelerometer only. The MPU-6050 features three 16-bit analog-to-digital converters (ADCs) for digitizing the accelerometer outputs. It also features a userprogrammable accelerometer full-scale range of f2g, f4g, f8g,and f16g. It can us I2C at 400kHz.
(b) Fig.1 (a)Architectural overview of wireless sensor hardware design. (b)IEEE 802.15.4 compliant wireless sensor node
A. Computational module The computational module is responsible for running the operation system. It contains the main application program, the data collection program and the data transportation program. It consists of a STMicroelectronics STM32F103T8 microcontroller with an 32-bit integrated circuit architecture, 64kB of flash memory, 20kB of static random access memory. It also has different kinds of internal buses like I2C, USART, and SPI. The STM32F103T8 requires a supply voltage of 3.3V. The remaining elements on board also operate at 3.3V, but the battery supply a voltage of 5V, so a 7533-1 voltage regulator is placed on board. The whole node is designed to be powered by a 18650 Li-battery
Fig.3 the sensor module
C. Wireless module The wireless module is responsible for transporting the data from BEE to PC for farther process. It consists of a Texas Instruments CC2530F256 wireless modem that operates within the 2.4GHz ISM communication band. It is rated for a line-of-site communications range of 150m. CC2530 is a single chip IEEE 802.15.4 compliant radio capable of providing communication with ranges adequate for civil infrastructures [2]. For example, its operational indoor communication range is validated up to 100m. Furthermore,
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the radio operates on the 2.4GHz internationally unregulated radio band with data rates as high as 250kpbs. The CC2530 requires a supply voltage of 3.3V. .
Fig.6 Topology map of star network
The IEEE 802.15.4 specification supports both star and peer-to-peer network topologies. In a star topology, one network device is designated as the central controller for communications (denoted as the personal area network(PAN) coordinator) while the remaining devices in the network communicate through it. Star networks operate independently of one another. In this study, star network is established, wherein the nodes join automatically.
Fig.4 the wireless module
IV. THE DATA COLLECTION AND ANALYSIS SYSTEM The data collection and analysis system (DCAS) consists of the program on PC and the program on nodes. The main function of the DCAS is collecting the data transmitted from the nodes, achieve analysis, and storage.
B. Work modes The DCAS is operated in both automated and useroperated modes. During automated operation, the DCAS sample for every nodes at sampling rate of 1024Hz continuously. The user-operated mode allows the user to select a number of sensors to collect data from at selected sampling rates. When user-operated, sampling rates ranging from 256 to 1024Hz are utilized. C. Work flow When the DCAS is running, all nodes begin to collect acceleration data automatically at sampling selected sampling rate, the total number of sampling points is also settable(the default value is 2048). The wireless time histories are recorded at selected sampling rates (the default value is 1024Hz). As it is proved above, the acceleration time-history recorded reveals the vibration response of the hull during a vibration event at roughly 2 seconds. Output acceleration time histories are converted to the frequency-domain through the use of standard discrete Fourier transform techniques (e.g., FFT, windowing, filtering) in nodes. When the server sends request to one node to transfer data. The node transfers frequency-domain data after receiving data transfer request. The time history responses recorded by DCAS are identical. This validates the high-resolution data collection capabilities of the DCAS. In addition, no data is lost during wireless communications.
Fig.5 System structure
A. Wireless topology The wireless sensor node proposed in this study is designed with distributed computing in mind. To facilitate distributed computing, the unit is programmed with a direct implementation of the IEEE 802.15.4 standard for wireless communication. The IEEE 802.15.4 standard defines the protocol and interconnectedness of wireless devices for flexible, Low-Rate, Wireless Personal Area Network (LPWPAN) [2]. The features of a LR-WPAN utilizing this standard are wireless connectivity over short ranges, low cost to build, easy installation, reliable data transfer, and efficient use of battery power. This is especially useful for sensor arrays, as the installed network is not limited in size or by specific component requirements. Sensors can be added to or removed from an existing network at will [10].
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D. Data analysis The output spectra are analyzed in order to identify modal peaks. All data transferred has been transformed to frequency domain. For the ship, the spectra are dominated at low frequency by the rigid body motion of the ship as it travels over waves. It should be noted that the accelerometers interfaced to the wireless hull monitoring system correspond to the central section of the ship. However, the DCAS has accelerometers at the four corners of the ship, as well as at the center of gravity, which would provide a more comprehensive view of the global operational deflection shape.
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Fig.8 User interface
The interface of the software is shown in Fig.8. As it shows, the functions of the DCAS include the displaying of the channels of the data acquisition, the display of the curve of the acquiring data, and the data storage and analysis. IV. RESULTS AND DISCUSSION In order to test the accuracy of the device, the BEE node was installed on a controllable vibration test instrument. The wireless vibration monitoring system effectively measures vibration during the test operation. Additionally, wireless sensors are capable of communicating data from distance with different frequencies of vibration. The outputs of the BEE node was showed in Fig.9. Fig.9(a),(b),(c) and (d) shows the output when the test instrument work at 25Hz,50Hz, 75Hz and 100Hz. As it is shown in the figure, the sensor node has a good accuracy and the communication module works well in vibration condition.
(b) Fig.7 (a)the flow chart of the node program (b)the flow chart of the server program
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generous support. This research was supported by National Natural Science Foundation of China (Grant No. 61100007, 61100081) and the collaborative research project under NSFCNRF cooperative Program (Grant No. 613111015). REFERENCES [1] Zimmerman, Andrew T., et al. "Automated modal parameter estimation by parallel processing within wireless monitoring systems." Journal of Infrastructure Systems 14.1 (2008): 102-113. [2] Swartz, R. Andrew, et al. "Design of a wireless sensor for scalable distributed in-network computation in a structural health monitoring system."5th international workshop on structural health monitoring. 2005. [3] Lynch, Jerome P., et al. "Monitoring of a high speed naval vessel using a wireless hull monitoring system." Proceedings of the Seventh International Workshop on Structural Health Monitoring. 2009. [4] Swartz, R. Andrew, and Jerome P. Lynch. "Strategic network utilization in a wireless structural control system for seismically excited structures." Journal of structural engineering 135.5 (2009): 597-608. [5] Swartz, R. Andrew, et al. "Structural monitoring of wind turbines using wireless sensor networks." Smart Structures and Systems 6.3 (2010): 183196. [6] Kim, Junhee, and Jerome P. Lynch. "Experimental analysis of vehicle– bridge interaction using a wireless monitoring system and a two-stage system identification technique." Mechanical Systems and Signal Processing 28 (2012): 3-19. [7] Ratcliffe, Colin, et al. "Investigation into the use of low cost MEMS accelerometers for vibration based damage detection." Composite Structures82.1 (2008): 61-70. [8] Cho, Soojin, et al. "Smart wireless sensor technology for structural health monitoring of civil structures." International Journal of Steel Structures 8.4 (2008): 267-275. [9] Swartz, R. Andrew, et al. "Hybrid wireless hull monitoring system for naval combat vessels." Structure and Infrastructure Engineering 8.7 (2012): 621-638. [10]Swartz, R. Andrew, et al. "Wireless Hull Monitoring Systems for Modal Analysis of Operational Naval Vessels." Proceedings of the International Modal Analysis Conference (IMAC) XXVII. 2009. [11]Stull, Christopher J., Christopher J. Earls, and Phaedon-Stelios Koutsourelakis. "Model-based structural health monitoring of naval ship hulls." Computer Methods in Applied Mechanics and Engineering 200.9 (2011): 1137-1149.
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(d) Fig.9 Outputs of the BEE node (a)25Hz (b)50Hz (c)75Hz (d)100Hz
V. CONCLUSIONS For the purpose of monitoring vibration condition in realtime, a wireless sensor system combining the MEMS accelerometer and WSN technology is designed in this study. The hardware structure and the software system have been proved. Wireless sensor networks provide a low-cost platform and it is easy to install. It has limited but enough ability to do on-board data processing. On every node of the wireless sensor network, fast Fourier Transformation and data wireless transportation are realized. The WSN system can collect all data from nodes and save all data in SQL database for farther processing. Future work should focus on some new directions. In the future work, modal analysis would be conducted using the acceleration data recorded by the DCAS during sealing. The output-only frequency domain decomposition (FDD) modal analysis method would be employed to derive the operational deflection shapes of the ship. While the operational deflection shapes will be strongly correlated to the ship’s mode shapes, the lack in knowledge of the system inputs prevents the analysis from deriving the mode shapes. ACKNOWLEDGMENT The authors would like to thank Mr.Lee Jingwei for his selfless help, he is really a great engineer that he always inspires me.The authors also would like to thank Miss.Han Meimei and Dr.Xiong Xinyan, they are superb programmers who we always rely on. Wei Fei thanks Dr.Fu Bin for his
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